We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of a cointegrating relation among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomena among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data, is less accurate than the prediction based on micro equations, and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading.

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.

The unprecedented volume of urban sensing data has allowed the tracking of individuals at remarkably high resolution. As an example, Telecommunication Service Providers (TSPs) cannot provide their service unless they continuously collect information regarding the location of their customers. In conjunction with appropriate post-processing methodologies, these traces can be augmented with additional dimensions such as the activity of the user or the transport mode used for the completion of journeys. However, justified privacy concerns have led to the enforcement of legal regulations aiming to hinder, if not entirely forbid, the use of such private information even for purely scientific purposes. One of the most widely applied methods for the communication of mobility information without raising anonymity concerns is the aggregation of trips in origin–destination (OD) matrices. Previous work has showcased the possibility to exploit multi-period and purpose-segmented ODs for the synthesis of realistic disaggregate tours. The current study extends this framework by incorporating the multimodality dimension into the framework. In particular, the study evaluates the potential of synthesizing multimodal, diurnal tours for the case where the available ODs are also segmented by the transport mode. In addition, the study proves the scalability of the method by evaluating its performance on a set of time period-, trip purpose-, and transport mode-segmented, large-scale ODs describing the mobility patterns for millions of citizens of the megacity of Tokyo, Japan. The resulting modeled tours utilized over 96% of the inputted trips and recreated the observed mobility traces with an accuracy exceeding 80%. The high accuracy of the framework establishes the potential to utilize privacy-safe, aggregate urban mobility data for the synthesis of highly informative and contextual disaggregate mobility information. Implications are significant since the creation of such granular mobility information from widely available data sources like aggregate ODs can prove particularly useful for deep explanatory analysis or for advanced transport modeling purposes (e.g., agent-based, microsimulation modeling).

Qualitative business survey data are used widely to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report up and down. This paper examines disaggregate or firm-level survey responses. It considers how the responses of the individual firms should be quantified and combined if the aim is to produce an early indication of official output data. Having linked firms' categorical responses to official data using ordered discrete-choice models, the paper proposes a statistically efficient means of combining the disparate estimates of aggregate output growth which can be constructed from the responses of individual firms. An application to firm-level survey data from the Confederation of British Industry shows that the proposed indicator can provide early estimates of output growth more accurately than traditional indicators.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use.

This has been clipped to the Gloucester PAE.

Dataset History

1. Joe Bell (GA) has clipped the surface water licences to the Gloucester PAE. This clip contains the works associated with the sharecomponent/ entitlement. This can be a many to many relationship. . A work is a surface water extraction point.

SW_licences_GloucesterPAE_Clip.dbf

1. Share component/ entitlement information was stored in the SW_Gloucester_COMBINED_v4.csv worksheet

2. Total volume of share component/ entitlement is 10,786ML

3. The works and share/component information was joined using Access, linking the CWlicence to the WAorCA_link. This links the volumetric entitlement to the works location.

4. This link also created share components that had a 0 entitlement which are licences that have been converted to unbundled licences in the new Water Act

5. By filtering out the 0 entitlement, the number of works linked to a share/component or entitlement with a specified volume was 212 with a total of 10,786ML. Worksheet FilteredIndividualSWLicences

6. Where there was more than one works per licence, an additional column was add COUNT_CWLICENSE. This shows where the share component/ entitlement is double counted as it is matched to each work with the full allocation.

7. An additional column was added SHARE_PER_WORKS which divides the share component/ entitlement by the number of works to give an allocation per works.

8. The SHARE_PER_WORKS column allows you to plot the works with the share component in ArcGIS without double counting the allocation.

9. A glossary of terms used ini the water licensing is included here: http://registers.water.nsw.gov.au/wma/Glossary.jsp

10. An additional worksheet was added to aggregate the data into Water Sources and Management Zones. The Water Sources and Management Zones were provided by NSW Office of Water

CombinedWSP_WSOURCES_31July2013.gdb\Geographic_GDA94\WSP_COMBINED_31July2013

1. The Avon River does not have management zones. Therefore data can only be viewed for the water source.

2. All other works can be aggregated to the Water Source, or the management zone depending on how you want to aggregate or disaggregate the data.

relevant fields:

CWLICENSE: works licence number

COUNT_CWLICENSE: Where there was more than one works per licence

SHARE_PER_WORKS: Share component divided by number of works to ensure no double counting

STATUS_DES: Status description as active, current, cancelled

LICENCE_iS: licensed issued date

LICENCE_LO: licence lodged date

LICENCE_P: Licence purpose eg. stock and domestic, town supply, irrigation

WORK_TYPE: pump, excavation etc

WORK_TYPE_: diversion or storage

MAJOR_CATC: major surface water catchement

NAME_OF_TH: water sharing plan the licence belongs to

WATER_SHAR: water sharing plan the licence belongs to

WATER_SOUR: water source

MANAGEMENT: management zone

WSP_STATUS: Status of the water sharing plan

START_DATE: Start date of the water sharing plan

END_DATE: end date of the water sharing plan

LICENSEorAPPROVAL: licence or approval number

Status: Cancelled or current (or blank)

ShareC: Share component attached to the licence

WAorCAlink:a combined water supply works / water use approval

LINKED_TO_AL:This is the identification number for an access licence which is shown on the licence certificate or on a search printout of the licence obtained from the access licence register run by Land and Property Information.

Dataset Citation

Bioregional Assessment Programme (XXXX) NSW Office of Water SW licences - Gloucester PAE v2 21022014. Bioregional Assessment Derived Dataset. Viewed 14 June 2016, http://data.bioregionalassessments.gov.au/dataset/f0a75a2b-233f-40a4-82cb-1929f2bee8c6.

Dataset Ancestors

• Derived From Subcatchment boundaries within and nearby the Gloucester subregion

• Derived From Bioregional Assessment areas v02

• Derived From Australian Coal Basins

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012.

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From BA ALL mean annual flow for NSW - Choudhury implementation of Budyko runoff v01

• Derived From Bioregional Assessment areas v01

• Derived From Geofabric Hydrology Reporting Catchments - V2.1

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From NSW Office of Water SurfaceWater licences in the Gloucester PAE

• Derived From GEODATA TOPO 250K Series 3

• Derived From Australian Geological Provinces, v02

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Gloucester Coal Basin

• Derived From Geological Provinces - Full Extent

• Derived From GLO Preliminary Assessment Extent

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

This paper uses disaggregate inflation data spanning all of consumption to examine: (i) the persistence of disaggregate inflation relative to aggregate inflation; (ii) the distribution of persistence across consumption sectors; and (iii) whether persistence has changed. Assuming mean inflation to be unchanged, disaggregate persistence inflation is consistently below aggregate persistence. Taking into account an early 1990s shift in mean inflation identified by break tests yields much lower estimates of both aggregate and disaggregate persistence for 1984-2002. But with the mean break, average disaggregate persistence is actually as great as aggregate inflation persistence. A factor model provides a natural framework for interpreting the relationship between aggregate and disaggregate persistence.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The difference between NSW Office of Water SW licences - NSSyd v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. The aim of this dataset was to be able to map each surface water works with the volumetric entitlement without double counting the volume and to aggregate/ disaggregate the data depending on the final use. This has not been been clipped to North and South Sydney PAEs. Dataset History The difference between NSW Office of Water SW licences - NSSyd v2 and v3 is that an additional column has been added, 'Asset Class' that aggregates the purpose of the licence into the set classes for the Asset Database. Where purpose = domestic; or domestic & stock; or stock then it was classed as 'basic water right'. Where it is listed as both a domestic/stock and a licensed use such as irrigation, it was classed as a 'water access right.' All other take and use were classed as a 'water access right'. Dataset Citation Bioregional Assessment Programme (2014) NSW Office of Water SW Offtakes Processed - North & South Sydney, v3 12032014. Bioregional Assessment Derived Dataset. Viewed 18 June 2018, http://data.bioregionalassessments.gov.au/dataset/7dc3a047-19a2-46ed-b519-b5e4f393aea1. Dataset Ancestors Derived From NSW Office of Water Surface Water Offtakes - North & South Sydney v1 24102013 Derived From NSW Office of Water SW Offtakes Processed - North & South Sydney, v2 07032014 Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

Gold nanoparticles linked to linear carboxylated dextran chains were attached to 3-aminopropyltriethoxysilane-functionalized glass surfaces. This method provides novel hybrid nanostructures on a surface with the unique optical properties of gold nanoparticles. The particles attached to the surface retain the capability to aggregate and disaggregate in response to their environment. This procedure presents an alternative method to the immobilization of gold nanoparticles onto planar substrates. Compared to gold nanoparticle monolayers, larger particle surface densities were obtained. Exposure to hydrophobic environments changes the conformation of the hydrophilic dextran chains, causing the gold nanoparticles to aggregate and inducing changes in the absorption spectrum such as red-shifting and broadening of the plasmon absorption peaks. These changes, characteristic of particle aggregation, are reversible. When the substrates are dried and then immersed in an aqueous environment, these changes can be visually observed in a reversible fashion and the sample changes color from the red color of colloidal gold to a bluish-purple color of aggregated nanoparticles. Surface-bound nanoparticles that retain their mobility when attached to a surface by means of a flexible polymer chain could expand the use of aggregation-based assays to solid substrates.